1. Field of the Invention
The invention relates to a method of creating a gamut boundary descriptor of the actual gamut boundary of a color device and to a method of gamut mapping source colors of a source color device into target colors of a target color device in a transformation color space using such method of creating a gamut boundary descriptor.
2. Description of Related Art
FIG. 1 shows the geometry of a sample two-dimensional gamut mapping (GM) algorithm that maps colors along a line from a source gamut of a color device using a Gamut Boundary Description (GBD) into a target gamut of another color device using another Gamut Boundary Description (GBD). Usually, GM algorithms determine intersections of the line with source and target GBDs and then determine a one-dimensional mapping function for each color on the line that indicates how far it should be moved along the line for a given color to be mapped. GBD are often based on a network of polygons in 3D space (2D GBD) or a polygon in 2D space (1D GBD) such as in FIG. 1. Such GBD are approximated or interpolated representations of the true or actual gamut boundary. In a simplest form, a GBD can consist in a number of vertices. In this case the GBD is a sampling of the true or actual gamut boundary.
U.S. Pat. No. 5,721,572 (see notably col. 2, lines 61-67) discloses a method of creating a gamut boundary descriptor (GBD) of the actual gamut boundary of a color device comprising the steps of:
in a transformation color space, selecting gamut boundary colors sampling the surface of said actual gamut boundary,
by using said selected gamut boundary colors, generating a network of elementary polygons representing said actual gamut boundary, each selected gamut boundary color belonging to an elementary polygon,
from said network of elementary polygons, generating said gamut boundary descriptor (GBD). In U.S. Pat. No. 5,721,572, each elementary polygon defines a local surface, called facet; see FIG. 8 of this document where these polygons are triangles.
More precisely, in the INRIA research report 3985 from August 2000 entitled “Smooth Surface Reconstruction via Natural Neighbour Interpolation of Distance Functions”, Jean-Daniel Boissonnat and Frédéric Cazals describe a more sophisticated generation of a gamut boundary descriptor by adding a so-called smoothing operation of an initial GBD made of the network of elementary polygons; such a method allows the creation of a gamut boundary descriptor that is closer to the actual gamut boundary than the basic network of elementary polygons that forms the initial GBD. The problem is that the interpolation algorithm smoothes also the shape singularities of the actual gamut boundary such its edges and summits, which leads to erroneously smoothed final GBD such as shown in FIG. 2. Often, GBD are then generated by interpolation or smoothing at intermediate positions (over sampled) or even approximated by curves or curved surfaces such as splines.
Summits in 1D GBD such as in FIG. 2 or summits and edges (line segments) in 2D GBD cannot be detected from the GBD itself. As shown in FIG. 2, it can usually not be known if one of the vertices represents a summit or not. For example, evaluating the “sharpness” of the surface of a GBD at a vertex is not sufficient. On the one hand, a vertex with very “sharp” surface can just be a normal vertex in a case where the sampling rate of the actual gamut boundary is too low. In this case, a smooth interpolation can even help. On the other hand, a vertex with smooth shape may represent a smooth summit.